Prediction of Postpartum Depression Using Multilayer Perceptrons and Pruning

S. Tortajada; J. M. García-Gómez; J. Vicente; J. Sanjuán; R. de Frutos; R. Martín-Santos; L. García-Esteve; I. Gornemann; A. Gutiérrez-Zotes; F. Canellas; Á. Carracedo; M. Gratacos; R. Guillamat; E. Baca-García; M. Robles

doi:10.3414/ME0562

Methods of Information in Medicine, Inhaltsverzeichnis

Methods Inf Med 2009; 48(03): 291-298
DOI: 10.3414/ME0562

Original Articles

Schattauer GmbH

Prediction of Postpartum Depression Using Multilayer Perceptrons and Pruning

Authors

S. Tortajada

¹IBIME, Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universidad Politécnica de Valencia, Valencia, Spain
J. M. García-Gómez

¹IBIME, Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universidad Politécnica de Valencia, Valencia, Spain
J. Vicente

¹IBIME, Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universidad Politécnica de Valencia, Valencia, Spain
J. Sanjuán

²Faculty of Medicine, Universidad de Valencia, Valencia CIBERSAM, Spain
R. de Frutos

²Faculty of Medicine, Universidad de Valencia, Valencia CIBERSAM, Spain
R. Martín-Santos

³IMIM-Hospital del Mar and ICN-Hospital Clínic, Barcelona CIBERSAM, Spain
L. García-Esteve

³IMIM-Hospital del Mar and ICN-Hospital Clínic, Barcelona CIBERSAM, Spain
I. Gornemann

⁴Hospital Carlos Haya, Málaga, Spain
A. Gutiérrez-Zotes

⁵Hospital Pere Mata, Reus, Spain
F. Canellas

⁶Hospital Son Dureta, Palma de Mallorca, Spain
Á. Carracedo

⁷National Genotyping Center, Hospital Clínico, Santiago de Compostela, Spain
M. Gratacos

⁸Center for Genomic Regulation, CRG, Barcelona, Spain
R. Guillamat

⁹Hospital Parc Tauli, Sabadell, Spain
E. Baca-García

¹⁰Hospital Jiménez Díaz, Madrid CIBERSAM, Spain
M. Robles

¹IBIME, Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universidad Politécnica de Valencia, Valencia, Spain

Abstract

Summary

Objective: The main goal of this paper is to obtain a classification model based on feed-forward multilayer perceptrons in order to improve postpartum depression prediction during the 32 weeks after childbirth with a high sensitivity and specificity and to develop a tool to be integrated in a decision support system for clinicians.

Materials and Methods: Multilayer perceptrons were trained on data from 1397 women who had just given birth, from seven Spanish general hospitals, including clinical, environmental and genetic variables. A prospective cohort study was made just after delivery, at 8 weeks and at 32 weeks after delivery. The models were evaluated with the geometric mean of accuracies using a hold-out strategy.

Results: Multilayer perceptrons showed good performance (high sensitivity and specificity) as predictive models for postpartum depression.

Conclusions: The use of these models in a decision support system can be clinically evaluated in future work. The analysis of the models by pruning leads to a qualitative interpretation of the influence of each variable in the interest of clinical protocols.

Keywords

Multilayer perceptron - neural network - pruning - postpartum depression

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Referenzen

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